Field
The present application relates to artificial intelligence systems used to analyze participants engaged in an activity and, in particular, to analysis of team behaviors using role and formation information.
Description of the Related Art
In possession-based sports, such as soccer and basketball, players typically maintain a spatial arrangement or “formation,” where a formation is a formal arrangement of a number of persons or things acting as a unit in order to optimize the chance of achieving one or more objectives. The formation is generally defined in terms of a set of roles, where players adopt specific roles associated with the formation. As play progresses, players may change or swap roles to achieve team objectives, such as to respond to the adversarial team's movements or to score a goal. A team may dynamically change from one formation to another, which may involve some or all players adopting a new role associated with the new position.
Vision-based player tracking systems have been deployed to track the movement of the players during a game or match. Tracking systems may also track the movement of an object of play, such as the position of the ball in possession-based sports. This tracking data is often archived over multiple games, whereby coaches, sports analysts, and broadcasters may access tracking data for entire games spanning one or more seasons of a given sport. In order to make use of this tracking data, the coach, analyst, or broadcaster first qualitatively determines the team formation, and the corresponding player roles, by examining the tracking data or video footage of the game. The coach, analyst, or broadcaster makes this qualitative determination based on experience and knowledge of the associated sport.
One drawback with the approach described above is that this process of manually identifying team formation and corresponding player roles is time consuming and prone to error. As one example, annotating one frame of player tracking data could take thirty seconds for basketball, where each team has five players, and one minute for soccer, where each team has ten players plus a goalkeeper. If each game has sixty minutes of play, and tracking data is recorded thirty times per second, then there would be 108,000 frames of tracking data to annotate for each game. In addition, different analysts could come to different conclusions about team formation and player roles for the same tracking data, based on differing levels of experience and expertise in sports analysis among the analysts. As a result, only a small sampling of the total number of tracking data frames would be annotated, and annotation results could be inconsistent across large tracking data sets—such as tracking data that covers a full season or more of play. Such inconsistency would hinder effective analysis of team behavior across multiple games.